Next product purchase and lapse predicting tool

ABSTRACT

A processor-based system and method retrieve customer purchase history information from an internal customer purchase history database for a plurality of customer records representing customers that previously purchased products of an enterprise, and retrieve customer profile information for each customer record. The processor executes a predictive machine learning model to determine a set of product purchase scores for each of the customers by applying a logistic regression model utilizing gradient boosting to the customer purchase history information and the customer profile information. The processor classifies the customers into a target customer group and a non-target customer group by applying a classification criterion to the set of product purchase scores, and generates a report of customers in the target customer group including highest product purchase scores and products recommended for cross-sale. In some embodiments, the predictive machine learning model is configured to forecast likelihood that given customers will lapse in payment.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of U.S. Ser. No.14/577,402, entitled “LAPSE PREDICTING TOOL AND SCORING MECHANISM TOTRIAGE CUSTOMER RETENTION APPROACHES,” filed Dec. 19, 2014, which claimsbenefit of U.S. Provisional Application No. 61/920,134, entitled “LAPSEPREDICTING TOOL AND SCORING MECHANISM TO TRIAGE CUSTOMER RETENTIONAPPROACHES,” filed Dec. 23, 2013, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure relates in general to management systems forfinancial products and, more specifically, to systems for customermarketing and customer relationship management.

BACKGROUND

Customers who have previously purchased insurance or other financialproducts from a diversified financial services business entity may havea need for additional products over time. An individual's insuranceneeds and needs for other financial products often vary based uponchanges in his or her risk profile and other personal circumstances. Thefinancial services business entity may offer a number of life insurance,retirement and investment products. This broad product offering may bedifficult to navigate for a customer, and it may be difficult for afinancial services business entity to identify especially suitableproducts for existing customers, and advantageous timing for offeringsuch products. Using conventional methods, to narrow the productofferings in a helpful and appropriate way, a financial servicesbusiness entity would need to conduct a careful needs analysis requiringthe existing customer to answer dozens of questions. Thus, there existsa need for methods of computationally developing a target list ofcustomers who are ready to purchase additional products, and methods ofcomputationally determining suitable products to offer such customers.Individuals purchase insurance products for a variety of reasons.Examples include ensuring payment of funeral services, providingadditional income to the individual's family in case of an accident, orproviding financial security to a loved one. Thus, for customers who areready to purchase additional products, there exists a need forcomputationally determining customer motivations for purchase.

Customers typically pay for purchased insurance products via periodicpayments, often through monthly or annual premiums. As individuals faceeconomic hardship, they may not pay one or more of said premiums and thepolicy may lapse. Generally, lapses in payments result in negotiationswith the individuals for continuing with an insurance service, orotherwise terminating the contract. Because of this, these individualsmay vary in their value as perceived by the insuring company. Theperceived value may play an important role during future transactions,including the discussion of future lapses, and marketing of additionalproducts. However, determining a policyholder's likelihood of lapse andthe value of a policyholder remains a challenge. Often, thedetermination of the individual's likelihood of lapse and value to thecompany is a labor-intensive, time-consuming endeavor. Thus, thereexists a need for methods of computationally predicting the likelihoodof lapse, and for promoting retention value of a policyholder or othercustomer owning a financial product.

SUMMARY

In an embodiment of a processor-based method, a processor executes apredictive machine learning model configured to determine, for eachcustomer record of a plurality of customer records including customerprofile data stored in a customer database, a set of product purchaseranks. The method inputs customer purchase history information and thecustomer profile data into a regression model utilizing gradientboosting. The predictive machine learning model outputs a first subsetof the plurality of customer records into a target group and a secondsubset of the plurality of customer records into a non-target groupbased upon the set of product purchase ranks. In an embodiment, each ofthe set of product purchase ranks is representative of a likelihood thata respective customer will accept an offer to purchase a respectiveproduct from the set of products of the enterprise.

In an embodiment, the predictive machine learning model is continuouslytrained using updated customer profile data and updated customerpurchase history information. The method runs the predictive machinelearning module on demand to update and display a graphical userinterface (GUI) by a display device in operative communication with theprocessor. The graphical user interface (GUI) includes the first subsetof the plurality of customer records in the target group, and furtherincludes, for each of the first subset of the plurality of customerrecords, a favored product from the set of products of the enterpriseselected by applying a classification criterion to the set of productpurchase ranks.

In an embodiment, a processor retrieves customer purchase historyinformation for a plurality of customer records for a population ofcustomers (also herein called plurality of customers) from an internalcustomer purchase history database of an enterprise. Additionally, theprocessor retrieves customer profile information for each customer fromone or more demographic databases. In various embodiments, the customerpurchase history information tracks previous purchase by each customerof one or more products of the enterprise. In various embodiments,demographic databases can include an internal database and external(third party) database.

In various embodiments, the system and method determine when existingcustomers of an enterprise are likely to purchase additional products.In an embodiment, the system and method classify each of the pluralityof customers into one of a target customer group and a non-targetcustomer group, by determining a highest product purchase score of theset of product purchase scores determined for the respective customerrecord.

In an embodiment, the predictive model is additionally configured toforecast, within the plurality of customers of the enterprise, thelikelihood that given customers will lapse in payment for the one ormore products previously purchased by the customer. The predictive modeldetermines a lapse rank representative of a likelihood of lapse by eachcustomer. Various embodiments classify each of the customers into one ofa target retention group with highest values of the lapse scores, and anon-target retention group, and generate a report of the customers inthe target retention group including the highest lapse scores.

In an embodiment, the graphical user interface (GUI) displays highestproduct purchase scores of the first subset of the plurality of customerrecords in the target group, and the products having the highest productpurchase scores.

In another embodiment, the customer purchase history information foreach of the plurality of customer records includes an initial productpurchase from the set of products of the enterprise, and a date of theinitial product purchase. The predictive machine learning model outputsa first subset of the plurality of customer records into a target groupand a second subset of the plurality of customer records into anon-target group based upon the set of product purchase ranks determinedby the predictive machine learning model. Based on this output, theprocessor updates at least some of the plurality of customer records inthe customer database to indicate whether the respective customer recordis included in the target group, or is included in the non-target group.

In an embodiment of a processor-based method, a processor executes apredictive machine learning model configured to determine, for eachcustomer record of a plurality of customer records including customerprofile data stored in a customer database of an enterprise, at leastone product purchase rank associated with at least one product of theenterprise. The method inputs historical purchase information and thecustomer profile data into a logistic regression model in combinationwith a decision tree model to continuously train the predictive machinelearning model to select a subset of features of the historical purchaseinformation and customer profile data.

The processor automatically generates motivation interpretability datafor at least one selected feature outputted from the predictive machinelearning model. While the processor is associated with an ongoingcommunication session with a customer device, the processor generates,for display by a user interface in operative communication with theprocessor, content comprising alphanumeric text or a visualizationgraphic based on the motivation interpretability data for the at leastone selected feature for a particular customer associated with thecustomer device.

In an embodiment, the processor-based method determines for each of thecustomer records a set of highest importance features of the subset offeatures of the historical purchase information and customer profiledata. The method automatically generates motivation interpretabilitydata for each of the highest importance features. The generates contentcomprising alphanumeric text or a visualization graphic based on themotivation interpretability data including the product purchase rank,the product of the enterprise, the set of highest importance features,and the motivation interpretability data for each of the highestimportance features.

In an embodiment of a processor-based method, a processor executes apredictive machine learning model configured to determine, for eachcustomer record of a plurality of customer records including customerprofile data stored in a customer database, a set of product purchaseranks. The method inputs historical customer purchase data and thecustomer profile data into a logistic regression model to continuouslytrain the predictive machine learning model to generate associationrules correlating previously purchased products from a set of productsof the enterprise with a potential purchase.

The method inputs into the predictive machine learning model a selectedcustomer profile to predict a highest likelihood potential purchase.While the processor is associated with an ongoing communication sessionwith a customer device, the processor generates, for display by a userinterface in operative communication with the processor, contentcomprising an indicator for the highest likelihood potential purchase.

In an embodiment, each of the generated association rules comprises anantecedent itemset of one or more of the previously purchased productsfrom the set of products of the enterprise, and a subsequent itemset ofthe current purchase of the additional product of the enterprise.

In an embodiment, the method utilizes market basket analysis to selectparticular association rules from the generated association rules basedon support thresholds representing a minimum frequency within aplurality of purchase history transactions in the customer purchasehistory database. In an embodiment, the method utilizes market basketanalysis to select particular association rules from the calculatedassociation rules based on confidence thresholds defining a minimumnumber of transactions in the customer purchase history database inwhich the antecedent itemset of the one or more of the previouslypurchased products appears.

In an embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine learning model configured to determine,for each customer record of a plurality of customer records includingcustomer profile data stored in a customer database, a set of productpurchase ranks by inputting customer purchase history information andthe customer profile data into a regression model utilizing gradientboosting, the predictive machine learning model outputting a firstsubset of the plurality of customer records into a target group and asecond subset of the plurality of customer records into a non-targetgroup based upon the set of product purchase ranks, wherein each of theset of product purchase ranks is representative of a likelihood that arespective customer will accept an offer to purchase a respectiveproduct from the set of products of the enterprise, and wherein thepredictive machine learning model is continuously trained using updatedcustomer profile data and updated customer purchase history information;and running the predictive machine learning module on demand to updateand display, by a display device in operative communication with theprocessor, a graphical user interface (GUI) including the first subsetof the plurality of customer records in the target group, and furtherincluding, for each of the first subset of the plurality of customerrecords, a favored product from the set of products of the enterpriseselected by applying a classification criterion to the set of productpurchase ranks.

In an embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine learning model configured to determine,for each customer record of a plurality of customer records includingcustomer profile data stored in a customer database, a set of productpurchase ranks by inputting customer purchase history information andthe customer profile data into a regression model utilizing gradientboosting, the predictive machine learning model outputting a firstsubset of the plurality of customer records into a target group and asecond subset of the plurality of customer records into a non-targetgroup based upon the set of product purchase ranks, wherein each of theset of product purchase ranks is representative of a likelihood that arespective customer will accept an offer to purchase a respectiveproduct from the set of products of the enterprise, wherein the customerpurchase history information for each of the plurality of customerrecords includes an initial product purchase from the set of products ofthe enterprise, and a date of the initial product purchase; and whereinthe predictive machine learning model is continuously trained usingupdated customer profile data and updated customer purchase historyinformation; and updating the plurality of customer records in thecustomer database to indicate whether the respective customer record isincluded in the target group or is included in the non-target group.

In an embodiment, a system comprises non-transitory machine-readablememory that stores customer records for a plurality of customers of anenterprise, and a customer purchase history database comprising purchasehistory information for the plurality of customers of the enterprise,said purchase history information comprising information on previouspurchase by each customer of one or more products from a set of productsof the enterprise; a predictive modeling module that stores a predictivemachine learning model configured to determine, for each of theplurality of customer records, a set of product purchase ranks byapplying a regression model utilizing gradient boosting, wherein each ofthe set of product purchase ranks is representative of a likelihood thata respective customer will accept an offer to purchase a respectiveproduct from the set of products of the enterprise; and a processor inoperative communication with the display, configured to execute acustomer targeting module, wherein the processor in communication withthe non-transitory, machine-readable memory and the predictive modelingmodule executes a set of instructions instructing the processor to: foreach of the plurality of customer records of the enterprise, determinethe set of product purchase ranks by inputting the customer purchasehistory information and the customer profile data into the predictivemachine learning model, output a first subset of the plurality ofcustomer records into a target group and a second subset of theplurality of customer records into a non-target group based upon the setof product purchase ranks determined; and running the predictive machinelearning module on demand to update a graphical user interface (GUI)including the first subset of the plurality of customer records in thetarget group, and further including, for each of the first subset of theplurality of customer records, a favored product from the set ofproducts of the enterprise selected by a highest product rank of the setof product purchase ranks.

In an embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine learning model configured to determine,for each customer record of a plurality of customer records includingcustomer profile data stored in a customer database of an enterprise, atleast one product purchase rank associated with at least one product ofthe enterprise by inputting historical purchase information and thecustomer profile data into a logistic regression model in combinationwith a decision tree model to continuously train the predictive machinelearning model to select a subset of features of the historical purchaseinformation and customer profile data; automatically generating, by theprocessor, motivation interpretability data for at least one selectedfeature outputted from the predictive machine learning model; and whilethe processor is associated with an ongoing communication session with acustomer device, generating, by the processor, for display by a userinterface in operative communication with the processor, contentcomprising alphanumeric text or a visualization graphic based on themotivation interpretability data for the at least one selected featurefor a particular customer associated with the customer device.

In an embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine learning model configured to determine,for each customer record of a plurality of customer records includingcustomer profile data stored in a customer database of an enterprise, atleast one product purchase rank associated with at least one product ofthe enterprise, and a lapse rank representative of a likelihood that arespective customer will lapse in payment for the one or more productsfrom the set of products of the enterprise previously purchased by therespective customer, by inputting the customer profile data andhistorical purchase information for the one or more products previouslypurchased by the respective customer into a logistic regression model incombination with a decision tree model to continuously train thepredictive machine learning model to select a subset of features of thehistorical purchase information and customer profile data, wherein thehistorical purchase history information comprises information onprevious purchase by each of the one or more customers of one or moreproducts from a set of products of the enterprise; automaticallygenerating, by the processor, motivation interpretability data for atleast one selected feature outputted from the predictive machinelearning model; and generating, by the processor, for display by a userinterface in operative communication with the processor, contentcomprising alphanumeric text or a visualization graphic based on themotivation interpretability data for the at least one selected feature.

In an embodiment, a system comprises non-transitory machine-readablememory that stores customer records including customer profile data fora plurality of customers of an enterprise, and a customer purchasehistory database comprising purchase history information for theplurality of customers of the enterprise, said purchase historyinformation comprising information on previous purchase by each customerof one or more products from a set of products of the enterprise; apredictive modeling module that stores a predictive machine learningmodel configured to determine, for each of the plurality of customerrecords, at least one product purchase rank associated with at least oneproduct of the enterprise by a logistic regression model in combinationwith a decision tree model, wherein each of the set of product purchaseranks is representative of a likelihood that a respective customer willaccept an offer to purchase a respective product from the set ofproducts of the enterprise; wherein the predictive machine learningmodel is continuously trained by inputting the historical purchaseinformation and the customer profile data into the logistic regressionmodel in combination with the decision tree model to select a subset offeatures of the historical purchase information and customer profiledata; a processor in operative communication with the display,configured to execute a customer targeting module, wherein the processorin communication with the non-transitory, machine-readable memory andthe predictive modeling module executes a set of instructionsinstructing the processor to: for each of the plurality of customerrecords of the enterprise, determine the at least one product purchaserank by inputting the customer purchase history information and thecustomer profile data into the predictive machine learning model,automatically generate motivation interpretability data for at least oneselected feature outputted from the predictive machine learning model;and while the processor is associated with an ongoing communicationsession with a customer device, generate for display by a user interfacein operative communication with the processor, content comprisingalphanumeric text or a visualization graphic based on the motivationinterpretability data for the at least one selected feature for aparticular customer associated with the customer device.

In an embodiment, a processor-based method comprises executing, by aprocessor, a predictive machine learning model configured to determine,for each customer record of a plurality of customer records includingcustomer profile data stored in a customer database, a set of productpurchase ranks by inputting historical customer purchase data and thecustomer profile data into a logistic regression model to continuouslytrain the predictive machine learning model to generate associationrules correlating previously purchased products from a set of productsof the enterprise with a potential purchase; inputting, by the processorinto the predictive machine learning model, a selected customer profileto predict a highest likelihood potential purchase; while the processoris associated with an ongoing communication session with a customerdevice, generating, by the processor, for display by a user interface inoperative communication with the processor, content comprising anindicator for the highest likelihood potential purchase.

In an embodiment, a system comprises non-transitory machine-readablememory that stores plurality of customer records including customerprofile data for customers of the enterprise and historical customerpurchase data for the plurality of customers of the enterprise, saidhistorical customer purchase data comprising information on previouspurchase by each customer of one or more products from a set of productsof the enterprise; a predictive modeling module that stores a predictivemachine learning model configured to determine, for each of one or morecustomer records, a set of product purchase ranks by applying a logisticregression model to continuously train the predictive machine learningmodel to generate association rules correlating previously purchasedproducts from the set of products of the enterprise with a potentialpurchase; and a processor, configured to execute a customer targetingmodule, wherein the processor in communication with the non-transitory,machine-readable memory and the predictive modeling module executes aset of instructions instructing the processor to: for each of theplurality of customers of the enterprise, determine the at least oneproduct purchase rank by inputting the customer purchase historyinformation and the customer profile data into the predictive machinelearning model; and while the processor is associated with an ongoingcommunication session with a customer device, automatically generate fordisplay by a user interface in operative communication with theprocessor, content comprising an indicator for the highest likelihoodpotential purchase.

Numerous other aspects, features and benefits of the present disclosuremay be made apparent from the following detailed description takentogether with the drawing figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be better understood by referring to thefollowing figures. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating the principles ofthe disclosure. In the figures, reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a functional block diagram illustrating a predictive modelpipeline, according to an embodiment.

FIG. 2 is a block schematic diagram of components of a predictive modelpipeline, according to an embodiment.

FIG. 3 is a block schematic diagram of components of a predictive modelpipeline, according to an embodiment.

FIG. 4 is a block schematic diagram of components of a predictive modelpipeline, according to an embodiment.

FIG. 5 is a representative view of a user interface of an agent'sdevice, showing a dashboard displaying a next product purchase (NPP)report.

FIG. 6 is a representative view of a user interface of an agent'sdevice, showing a dashboard displaying a lapse report.

FIG. 7 is a representative view of a user interface of an agent'sdevice, showing a dashboard displaying graphical visualization datarelating to next product purchase (NPP).

FIG. 8 is a block diagram illustrating an exemplary computing device inwhich one or more embodiments of the present disclosure may operate,according to an embodiment.

FIG. 9 is a flow chart diagram of a method for predicting and reportingcustomers with greatest likelihood of next product purchase (NPP) and/orlapse behaviors.

FIG. 10 is a graph of a receiver operator curve (ROC) for a logisticregression model for predicting likelihood of cross-sale of the productgroup PERM, according to an embodiment.

FIG. 11 is a graph of a receiver operator curve (ROC) for a gradientboosting model for predicting likelihood of cross-sale of the productgroup TERM, according to an embodiment.

DETAILED DESCRIPTION

The present disclosure is here described in detail with reference toembodiments illustrated in the drawings, which form a part hereof. Otherembodiments may be used and/or other changes may be made withoutdeparting from the spirit or scope of the present disclosure. Theillustrative embodiments described in the detailed description are notmeant to be limiting of the subject matter presented here.

As used here, the following terms may have the following definitions:

“Customers” refers to entities, generally individuals, who own one ormore products sold by an enterprise. In an embodiment, the enterprise isa financial services entity such as an insuring company. Customersinclude, but are not limited to, policyholders who hold one or morepolicies sold by an insuring company. Alternatively, customers arereferred to herein as existing customers, or as product owners.“Customer Record” refers to a record in an internal database of anenterprise or in an external database with information such as customerprofile data pertaining to a particular customer.

“Agent” refers to an individual working for a financial services companyor working as a broker for a financial services company, in customerrelationship management. In an embodiment, an agent may represent anintermediary between an issuing company and a customer. In anembodiment, “agent” refers to an insurance agent, who may sell a varietyof insurance products as well as other financial products.

“Products” refers to insurance policies and other financial productsoffered by a financial services entity. Products include, e.g.,financial products owned by customers of the financial services entity,and products that the financial services company offers to itscustomers. In an embodiment, “products” include a variety of insuranceproducts, including property insurance and casualty insurance, lifeinsurance, health insurance, disability insurance, and long-term careinsurance, as well as other financial products such as annuities.

“Policy” refers to a contract insuring a person or goods.

“Policyholder” refers to any entity that holds one or more insurancepolicies.

“Premium” refers to a payment, or one of the periodic payments, apolicyowner agrees to make for a policy. Depending on the terms of thepolicy, the premium may be paid in one payment or a series of regularpayments, e.g., annually, semi-annually, quarterly or monthly.

“Rider” refers to an amendment to a policy contract. In an embodiment,riders provide additional benefits to an insurance policy, and mayrequire an additional premium.

“Lapse” refers to the inability of a policyholder or other owner of afinancial product to pay, at a time previously agreed upon with a policyissuer, a premium or other cost associated with a policy or otherfinancial services product held.

“Face Value” refers to the value of a policy or other financial productto be provided upon maturity, date, or death.

“Lifetime Value” refers to the sum of all associated costs of afinancial services entity for a product owned by a customer over productlifetime, netted against revenue for the product sale.

“Value of Retention” refers to a potential value of maintaining businessassociated with a policyholder or other customer.

“Cross-Selling” refers to marketing and “Cross-Sale” refers to a sale toa customer that owns one or more products of an enterprise, of anadditional product of the enterprise. In an embodiment, “cross-sale”refers to a sale at least six months after the customer's date ofinitial product, of a product in a different major product family thanany previously purchased product.

“Up-Selling” refers to marketing and sale to a customer that owns aproduct of an enterprise with a face value, of an additional face valueof the product owned.

The present disclosure addresses a need for computer-implemented methodsto forecast actions by customers of an enterprise who own one or moreproducts of an enterprise, wherein the actions may significantly improvethe lifetime value of these customers to the enterprise. Thesepredictive models forecast the likelihood of a customer's purchasingadditional products and/or additional face amounts of previouslypurchased products, of the enterprise. Additionally, these predictivemodels forecast the likelihood of a customer's lapsing in payments for apolicy, or other product of the enterprise, owned by the customer.

In an embodiment, agents representing an enterprise can use suchforecasts to identify customers within a population of customers whohave purchased one or products of the enterprise, wherein thesecustomers have the highest likelihood of beneficial action ordetrimental action, in order to target these customers for marketingactions to encourage the beneficial action or discourage the detrimentalaction. In an embodiment, agents receive forecasts from a predictivemodel to identify customers with high probability of purchasingadditional products of the enterprise, sometimes herein called “nextproduct purchase” (NPP). In an embodiment, agents use forecasts from apredictive model to identify customers with high probability of lapse,i.e., failure to pay premium or other costs associated with a product.

In an embodiment of a processor-based method, a processor receivescustomer purchase history information (also herein called purchasehistory information) for a population of customers from an internalcustomer purchase history database of an enterprise. The customerpurchase history information tracks previous purchases by each customerof one or more products of the enterprise. Additionally, the processorreceives customer profile information for each customer from one or moredemographic databases. In an embodiment, the demographic databasesinclude an internal database of the enterprise and an external(third-party) database.

The processor executes a predictive model configured to determine, foreach of the plurality of customers, a set of product purchase scores byapplying an NPP predictive model to the purchase history information andthe customer profile information. In an embodiment, the predictive modelcomprises a combination of gradient boosting with a regression modelthat determines when existing customers of an enterprise are likely topurchase additional products. In an embodiment, the regression modeluses logistic regression.

The processor-based method of the present disclosure classifies each ofthe plurality of customers into one of a target customer group and anon-target customer group, by applying a classification criterion to theset of product purchase scores determined for the respective customer.The method then generates, for display by a user interface in operativecommunication with the processor, a report of the customers in thetarget customer group including the highest product purchase scores andthe products having the highest product purchase scores. In anembodiment, the NPP predictive model exposes customers who lack productsthat the customers are likely to purchase. In various embodiments, theNPP model identifies products that customers with similar customerprofiles already possess. In an embodiment, the NPP model identifiessignificant relationships between products already owned by customersand target products that are likely to be purchased by the customers. Inthe present disclosure, a product identified by the NPP model as likelyto be purchased by a customer is sometimes called a “favored” product.

In an embodiment, the predictive model is additionally configured toforecast, within the population of customers of the enterprise, thelikelihood that given customers will lapse in payment for the one ormore products previously purchased by the customer. In an embodiment,the predictive model determines for each customer within the populationa lapse score representative of a likelihood of lapse by respectivecustomer. The processor-based method classifies each of the customersinto one of a target retention group with highest values of the lapsescores, and a non-target retention group; and generates a report of thecustomers in the target retention group including the highest lapsescores.

In an embodiment, a processor-based method retrieves purchase historyinformation for one or more identified customers from an internalcustomer purchase history database of an enterprise. Additionally, theprocessor retrieves customer profile information for each of thecustomers from one or more demographic databases. The processor executesa predictive model to determine, for each of the customers, at least oneproduct purchase rank associated with at least one product of theenterprise. The predictive model applies a logistic regression model incombination with a decision tree model to the purchase historyinformation and the customer profile information. In an embodiment, thelogistic regression model was previously trained by selecting a subsetof features of the customer purchase history information and of thecustomer profile information.

As used in the present disclosure, a product purchase rank can include araw product purchase score. In an embodiment, a higher product purchasescore may indicate a higher probability of a successful cross sale. Inanother embodiment, a product purchase rank incudes a tier correspondingto a given product purchase score, wherein the tier is selected from aplurality of tiers that are based upon a distribution of productpurchase scores for a population of customers of the enterprise. Forexample, “low”, “medium” and “high” tiers may represent differentsegments or tiers within the distribution of product purchase scores. Inan embodiment, a product purchase rank includes a percentileclassification of a given product purchase score relative to all productpurchase scores for a population of customers of the enterprise. In anembodiment, a product purchase rank can include a combination of theabove types of rank.

In an embodiment, the processor-based method determines for each of thecustomers a set of highest importance features of the subset offeatures. The method automatically generates motivation interpretabilitydata for each of the highest importance features. The system and methodupdates a graphical user interface (GUI) generate a report for each ofthe one or more customers, for display by a user interface in operativecommunication with the processor, for example to generate a report foreach of the one or more customers. The report includes the productpurchase rank, the product of the enterprise, the set of highestimportance features, and the motivation interpretability data for eachof the highest importance features.

In an embodiment, the logistic regression model is trained by selectingthe subset of features using a recursive feature elimination mechanismof the decision tree model. In an embodiment, the set of highestimportance features comprises features with highest absolute value ofimportance coefficients.

In an embodiment, the motivation interpretability data comprises one ormore of explanatory text data and visualization graphic data. In anexemplary embodiment, the motivation interpretability data providesinformation on a product of the enterprise as a recommended product fora customer, and describes motivations of the customer to purchase therecommended product. In another exemplary embodiment, the motivationinterpretability data describe reasons for variations in productpurchase rank of different customers in the book of business of an agentof the enterprise.

In an embodiment, a logistic regression model, which is configured todetermine at least one product purchase rank associated with at leastone product of the enterprise, is trained by performing market basketanalysis mining on data in an internal customer purchase historydatabase of the enterprise. The market basket analysis calculates aplurality of association rules correlating previously purchased productsfrom the set of products of the enterprise with a current purchase of anadditional product of the enterprise.

In an embodiment, each of the calculated association rules comprises anantecedent itemset of one or more of the previously purchased productsfrom the set of products of the enterprise, and a subsequent itemset ofthe current purchase of the additional product of the enterprise.

In an embodiment, the market basket analysis selects particularassociation rules from the calculated association rules based on supportthresholds representing a minimum frequency within a purchase historytransactions in the customer purchase history database. In anembodiment, the market basket analysis selects particular associationrules from the calculated association rules based on confidencethresholds defining a minimum number of transactions in the customerpurchase history database in which the antecedent itemset of the one ormore of the previously purchased products appears.

In an embodiment, during training of the predictive module, the marketbasket analysis mines purchase history data from an internal customerpurchase history database of the enterprise that tracks, for eachcustomer of the enterprise, the initial product purchased and date offirst purchase, and new and cumulative purchases of products of theenterprise during customer-years commencing from anniversaries of thecustomer's date of first purchase. In an embodiment, the market basketanalysis treats each individual customer year as a negative label withintraining data, and treats each sale year as a positive label withintraining data.

In an embodiment, the predictive model (also called predictive machinelearning model in the present disclosure) periodically forecasts nextproduct purchases (NPP) and/or lapses for a population including allcurrent customers of the enterprise. In an embodiment, an agent of theenterprise can select customers within a book of business of the agent,and run NPP and/or lapse reports to plan marketing and sales activities.In an embodiment, an NPP report presents information generated by thepredictive model on products having the highest product purchase scoresas recommended products for the respective customers. The recommendedproducts can be different from one or more products previously purchasedby the respective customers. The recommended products also can includean additional face amount of one of one or more products previouslypurchased the respective customers (up-selling).

In various embodiments, the method and apparatus of the inventionoperate in conjunction with a customer relationship management (CRM)platform that is used by agents to interact the customers of theenterprise. In an embodiment, the CRM platform updates and displays agraphical user interface (GUI) representing topics relating to predictednext product purchases, enabling the agents to discuss these topics incross-selling of an additional product or up-selling of an additionalface amount. As used herein, agent-customer contacts aided by suchinformation are sometimes called “treatments.” NPP reports anddashboards can improve agent productivity by helping agents plan theamount of time to spend on cross-selling versus other activities; and byincreasing agents' conversion rate for customers who are engaged.

In various embodiments, the system initiates an ongoing communicationsession with a client device of a customer in the target customer groupto display a graphical user interface (GUI) that can present a favoredproduct, and motivating factors for the customer to purchase the favoredproduct. The ongoing communication session can include email, chat,texting, over-the-top messaging, or others.

The methods and systems of the present disclosure utilize product dataon a set of products of the enterprise. In an embodiment, such productdata is stored in an internal product database of the enterprise. In anembodiment, products of the enterprise include major products, alsoherein called product types. Products of the enterprise also includeminor products, which are subsets of the major products. Additionally,products of the enterprise may include tertiary or additional subsets ofproducts of the enterprise. In an embodiment, in predictive modeling,lower levels of product categories are conflated into product classes.In an embodiment, the system retrieves product data from the internalproduct database of the enterprise, and includes the retrieved productdata in reports generated by the predictive model for the customers inthe target customer group.

In an embodiment, products of the enterprise include riders. Riders canprovide extra benefits or options added onto policies. In an embodiment,one product feature, waiver of premium rider (“wp”), was found to beconsistently predictive of cross-sale. A waiver of premium rider waivespolicyholders' obligation to pay premiums if they become seriously ill.Other exemplary product features included risk class, deferral period,period certain duration, and total net payout for Fixed Annuities. Invarious embodiments, risk class was based on mortality for lifeproducts, and was based on occupation for disability products.

In an example of major and minor products shown in Table 1, majorproduct types (Major_product) included PERM (permanent life insurance),TERM (term insurance), DIS INC (disability income), FA (fixed annuity),and NTL (non-traditional life). Exemplary minor products (Minor_product)within these product types are shown in the column Minor_product/ProductName. In an embodiment, the NPP predictive model included a componentpredictive model for each of the Major_product types of Table 1. Thepredictive machine learning model utilized purchase history informationfor additional product types, but did not generate product purchasescores for these other products. In an exemplary embodiment, otherproduct types used in purchase history information include MF (mutualfunds), BR (brokerages), MMGR (money manager), and VAR (variableannuity).

TABLE 1 MAJOR PRODUCTS & MINOR PRODUCTS Major_productMinor_product/Product Name PERM Whole Life Legacy 100 Whole Life Legacy65 Whole Life TERM Vantage Term 20 20 Year Level Term Guaranteed VantageTerm - 10 DIS INC Radius Base MaxElect Simplified Flex Elect Base FAMassMutual Odyssey MassMutual Odyssey Plus MassMutual Odyssey Select NTLUniversal Life Guard 2 Universal Life Guard Variable Universal Life 2

A key metric for value-based classification of a customer who haspurchased a product is herein called a “lifetime value” of the productsale to that customer. In various embodiments, lifetime value includesthe sum of all associated costs over product lifetime, netted againstrevenue (e.g., premiums paid) for the product sold. In an exemplaryembodiment involving sale of an insurance policy, associated costsinclude various sales acquisition costs, including marketing costsdistributed across inbound calls, cost of operating the inbound contactcenter distributed across inbound calls, and commission at the time ofsale. In this example, additional associated costs include cost ofproviding the insurance policy, and claims or death benefits. In anexemplary embodiment of sale of a policy, lifetime value for the policysold to that customer is the net value of all premiums paid over the sumof all such associated costs during that policy life. In an embodiment,agents of the enterprise pursue marketing and sales activities basedupon modeled lifetime values in computer-generated NPP reports, orcomputer-generated lapse reports, to increase the value of targetedcustomers. In an embodiment, computer-generated lapse reports ascribe avalue of retention to customers, and include various modeled lifetimevalues generated based on different assumptions, such as differentpricing assumptions for negotiation of policy renewal.

In various embodiments, agents may increase lifetime value throughcross-selling or up-selling to customers based on graphical userinterfaces (GUIs) updated and displayed by NPP predictive machinelearning models. Such GUIs, and CRM reports and dashboards including theGUIs, enable targeting of customers with a high readiness to purchase,and enable targeted product selection for marketing to such customers.Agents also may increase lifetime value through actions aimed atmitigating loss of lifetime value due to prospective lapse.

Frequency of successful cross-sales is one metric for performance of theNPP predictive models in aiding marketing and sales activities by agentsof the enterprise. In an embodiment, a successful cross-sale is definedas a sale of an additional product in a different major product familythan any product previously purchased by the customer, wherein that saleis effected at least six months after the customer's date of firstpurchase. By defining a first purchase history period that starts at thefirst purchase and spans six months, rather than a year, sales occurringless than 6 months after the initial purchase are considered a bundledpurchase rather than a cross-sale.

FIG. 1 is a schematic diagram of a predictive model pipeline 100.Predictive model pipeline 100 can be executed by a server, one or moreserver computers, authorized client computing devices, smartphones,desktop computers, laptop computers, tablet computers, PDAs and othertypes of processor-controlled devices that receive, process, and/ortransmit digital data. Predictive model pipeline 100 can be implementedusing a single-processor system including one processor, or amulti-processor system including any number of suitable processors thatmay be employed to provide for parallel and/or sequential execution ofone or more portions of the techniques described herein. Predictivemodel pipeline 100 performs these operations as a result of centralprocessing unit executing software instructions contained within acomputer-readable medium, such as within memory. In one embodiment, thesoftware instructions of the system are read into memory associated withthe predictive model pipeline 100 from another memory location, such asfrom a storage device, or from another computing device viacommunication interface. In this embodiment, the software instructionscontained within memory instruct the predictive model pipeline 100 toperform processes described below. Alternatively, hardwired circuitrymay be used in place of or in combination with software instructions toimplement the processes described herein. Thus, implementationsdescribed herein are not limited to any specific combinations ofhardware circuitry and software.

Data flow in predictive model pipeline 100 starts with enterprisedatabase 110. In an embodiment, enterprise database 100 includes, amongother data, product data, purchase history information data for productowners, and customer profile information for product owners. Exemplaryproduct data sourced from enterprise database 110 include specificproduct type; information on riders; financial data on face amount,premiums, and benefits; and risk class data such as mortality andoccupation data. Customer purchase history information includesinformation on customer's first purchase and date of first purchase, andinformation on new and cumulative purchases during each customer-yearfollowing date of first purchase. Exemplary customer profile informationstored in enterprise database includes age, gender, information about aninsured and beneficiaries. In an exemplary embodiment, enterprisedatabase 100 uses TERADATA® massively parallel processing (MPP)enterprise data platforms of Teradata Corporation, Miamisburg, Ohio.TERADATA® is a registered trademark of Teradata US, Inc.

In NPP predictive model pipeline 100, data retrieved from enterprisedatabase 110 is supplied to customer purchase history database 120 andcurrent customer snapshot database 130. An important aspect ofpredicting readiness to purchase in the “next product purchase” (NPP)predictive model is timing of purchases. The purchase history database120 stores purchase history information, also herein called productowner purchase history, for the population of customers to be modeled.In an embodiment, product owner purchase histories track purchases ofeach customer started from the product owner's first purchase andinclude all new and cumulative purchases. In an embodiment, an annualchronology of purchase history tracks purchases by customer-year. Thefirst year (Year 1) starts with the date of first purchase. Year 2commences at the one-year anniversary of the date of first purchase,etc. In an embodiment, purchase history information also trackspurchases within the first six months after the date of first purchaseseparately. Applicant has observed that NPP predictive models candistinguish between customer-years in which customers convert to across-sale and customer-years in which they do not, with high modelperformance.

In an embodiment, instances of customer-year in customer purchasehistory database 120 use a longitudinal data format. In an embodiment,the enterprise collected purchase history information for all currentcustomers, and constructed a longitudinal data set over different linesof business (different major and minor products). Longitudinal datatracks the same sample at different points in time. In an embodiment, arow of data in customer purchase history database 120 contains asnapshot of cumulative information about the customer and purchasedproducts for one customer-year. In an embodiment, customer purchasehistory data tracks owner-level features and product-level features. Inan exemplary embodiment, owner-level features, such as age and gender,were populated for every customer-year, while product-level featureswere only populated for customer-years in which the customer purchasedthat product.

In various embodiments, product-level features in customer purchasehistory database 120 are organized by columns representing specificproduct types. In an embodiment, purchase history data for a givenproduct type during a given customer-year use a Boolean denoting whetheror not at least one purchase of that product type was made during thatcustomer-year. In an embodiment, purchase history data also includescumulative indicators for product ownership, e.g., data indicating thatthe given customer has purchased a given product during a previous timeperiod.

Current customer snapshot database 130 contains current customer profiledata such as age, gender and financial standing. Current customerprofile data can be significant predictors in the predictive models ofthe present disclosure. Often such features are time dependent inrelation to a customer's needs and likelihood to purchase givenproducts. For example, a customer's first product typically fits thecustomer's needs at the time of first purchase. Over time, as thecustomer's profile changes, another product may better suit thecustomer's needs. Additional exemplary customer-level features includesocial security number, and median income by zip code.

In an embodiment, in addition to customer profile data extracted fromenterprise database 110, current customer snapshot database 130 containsdata retrieved from a third-party demographic database (not shown).Third-party demographic data can include extensive details of customerprofiles not tracked in an enterprise's internal customer profiledatabase. Examples of such third-party demographic data include generalpurchasing behaviors, credit worthiness, and products purchased fromproviders other than the enterprise. In an embodiment, current customersnapshot database retrieves data from the ACXIOM® customer demographicsdatabase maintained by Acxiom Corporation, Little Rock, Ark. ACXIOM® isa registered trademark of Acxiom Corporation.

In an embodiment, ACXIOM customer demographics data include individuallevel data on customers. In various embodiments, as a prerequisite tousing ACXIOM data in predictive modeling of a customer), currentcustomer snapshot database 130 associates the ACXIOM data with acustomer identifier for the customer. In an exemplary embodiment, Acxiomcustomer demographics data used in modeling of a customer requires anexact match of name and address. In an embodiment, ACXIOM customerdemographics data also include data using zip-level features of theACXIOM system, which provide a coarser representation in building thepredictive model. Such zip-level features employ variables in Acxiomthat have resolution at the zip-level for each individual in the zipcode. In an exemplary embodiment, zip-level data for individual incomeis associated with a zip code median value in Acxiom.

Databases 110, 120, 130 are organized collections of data, stored innon-transitory machine-readable storage. In an embodiment, the databasesmay execute or may be managed by database management systems (DBMS),which may be computer software applications that interact with users,other applications, and the database itself, to capture (e.g., storedata, update data) and analyze data (e.g., query data, execute dataanalysis algorithms). In some cases, the DBMS may execute or facilitatethe definition, creation, querying, updating and/or administration ofdatabases. The databases may conform to a well-known structuralrepresentational model, such as relational databases, object-orienteddatabases and network databases. Exemplary database management systemsinclude MySQL, PostgreSQL, SQLite, Microsoft SQL Server, MicrosoftAccess, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base, FileMakerPro.

In an embodiment, predictive model pipeline 100 labels each data elementstored in ACXIOM as continuous (including interval), binary, ordinal, ornominal (categorical). For use in logistic regression models withinpredictive modeling module 140, variables that have lookup fields areconverted to integers. Following feature transformation of the ACXIOMvariables, the final view outputs each variable with human-readablenames (if known), and a tag at the end of the variable name. Exemplaryend tags for transformed variable names include:

-   -   _binary: either 0 or 1    -   _ordinal_to_binary: either 0 or 1, where null values are mapped        to 0    -   _flat_binary: mapped from a string field like “01001000” into        multiple fields    -   _ordinal: as an integer, with null values left null    -   _interval: as an integer, with null values left null    -   _continuous: as an integer, with null values left null    -   _nominal: as an integer, with null values mapped to an        additional integer

Predictive modeling module 140 applied a logistic regression modelutilizing gradient boosting to couple optimal performance withinterpretable results. Models are trained on a set of historicalcustomer-year instances and then used to score the susceptibility ofcustomers to cross-sale during their most recent customer-year, whichcorresponds to the present date. In an embodiment, the predictivemodeling module trains a logistic regression model with a full set offeatures of databases 120, 130.

In an embodiment, predictive modeling module 140 includes a plurality ofcomponent predictive models that determine a set of product purchasescores 150. Each of the component predictive models targets likelihoodof purchasing one of the respective products from the set of products ofthe enterprise, and determines a respective product purchase scorewithin the set of product purchase scores.

Product purchase scores module 150 is configured to provides a set ofproduct purchase scores for each customer. In an embodiment, in applyingthe predictive modeling module to a given customer, product purchasescores generated by all component predictive models that correspond toproducts not already owned by the customer are compared to determine themost likely product for next product purchase. In an embodiment, productpurchase scores module provides one or more of a PERM score, TERM score,DIS INC score, FA score, and NTL score for each customer. In anembodiment, product purchase scores module 150 is also configured toprovide a lapse score for each customer modeled, in addition to the setof product purchase scores.

An API of the predictive modeling module 140 and the product purchasescores module 150 interfaces with CRM platform 160. In an embodiment,the API routes updated graphical user interfaces (GUIs) generated by thepredictive model to the CRM platform, and routes customer relationshipdata from the CRM platform to the predictive model. In an embodiment,GUIs generated by the predictive model are displayed at user interfacesof agent devices 170, 180. In an embodiment, the GUIs are displayed toagents of the enterprise via one or more report or dashboard of thecustomer relationship management (CRM) platform 160. In an embodiment,CRM platform 160 is the SALESFORCE® cloud computing customerrelationship management (CRM) platform of Salesforce.com, Inc., SanFrancisco, Calif., which provides users with an interface for casemanagement and task management, and a system for automatically routingand escalating important events. SALESFORCE® is a registered trademarkof Salesforce.com, Inc., San Francisco, Calif.

Use of logistic regression for classification problems providesperformance advantages over standard linear regression, becauseapplication of the logistic function to the raw model score maps theoutput precisely from 0→1 while providing a smooth decision boundary. Inan embodiment, a logistic regression model with l₁ regularizationutilizes LASSO (Least Absolute Shrinkage and Selection Operator), aregression analysis method that performs both variable selection andregularization to enhance prediction accuracy and ease of interpretationof the resulting statistical model.

For compatibility with logistic regression, a procedure for training thepredictive machine learning model discretized variables, treating eachbin as a distinct level of a categorical variable, including a bin formissing values. An adjacent pooling bucketing scheme was used to createbins that would provide meaningful interpretations of scores. Adjacentpooling is a bivariate approach that uses both target variable valuesand numeric covariate values to determine optimal bucketing. Binarytarget variables and numeric covariate values were extracted and thenumeric covariates were used to bucket the records equally into manyinitial buckets (e.g., 50 buckets). Then, for each pair of adjacentbuckets, the buckets were combined before calculating a difference ininformation value, defined as:Information Value=Σ_(k=1) ^(k)(g _(k) −b _(k))*log(g _(k) /b _(k))

In the above formula, g_(k) and b_(k) are the ratios of positive andnegative labels in each bucket out of all k buckets. In an exemplaryembodiment, the first pass on a variable split the records into 50 equalbuckets. Then 49 different adjacent pairs were compared to select thecombination of pairs that resulted in the least data reduction. Theprocess was continued until each variable was separated into the desirednumber of buckets, usually 10 or fewer.

Gradient boosting produces a predictive model in the form of an ensembleof weak predictive models, typically decision trees. In an embodiment,predictive modeling module 140 incorporated the GBM package cgbm(Generalized Boosted Regression modeling) for gradient boosting. In anexemplary embodiment, gradient boosting used 10,000 decision trees, aminimum number of observations per terminal node of 10, and aninteraction depth of 10 to test gradient boosting for all product types.

In another embodiment, a decision tree model is a random forests model.Random forests is a class of ensemble methods used for classificationproblems. Random forests models work by fitting an ensemble of decisiontree classifiers on sub samples of the data. Each tree only sees aportion of the data, drawing samples of equal size with replacement.Each tree can use only a limited number of features. By averaging theoutput of classification across the ensemble, the random forests modelcan limit over-fitting that might otherwise occur in a decision treemodel.

In various embodiments, predictive machine learning modeling module 140identifies high importance features that have the most pronounced impacton predicted value, i.e., highest importance coefficient. In anembodiment, a logistic regression model is trained by selecting a subsetof features of the purchase history information stored by the internalcustomer purchase history database and of the customer profileinformation stored by the customer demographic database(s).

TABLE 2 Highest Importance Features Importance Feature −2.7125expectant_parent_nominal −0.3126 recent_divorce_nominal_0 −0.2634credit_card_new_issue_nominal_0 −0.1438gender_input_individual_nominal_0 0.1117 socially_influenced_ordinal0.0890 home_length_of_residence_interval −0.0757likely_investors_nominal_0 −0.0667vacation_travel_international_would_enjoy_ordinal_to_binary 0.0637total_liquid_investible_assets_fin_ordinal −0.0632 new_mover_nominal_0−0.0518 single_parent_ordinal_to_binary −0.0517vacation_travel_time_share_have_taken_ordinal_to_binary −0.0455investments_real_estate_ordinal_to_binary 0.0438investments_stocks_bonds_ordinal_to_binary 0.0429obtain_life_insurance_along_with_loan_mortgage_installment_payments_ordinal

Table 2 shows the top 15 features from a likelihood to purchase model.The most important features are identified by the highest absolute valueof the importance coefficient. The most important feature of this targetis the expectant_parent_nominal variable, where a 0 corresponds to notexpectant. Positive and negative signs of the importance coefficientindicate whether an increases, or a decrease, of the feature increaseslikelihood of the target. This data indicates that non-expectant parentsare less likely to purchase. This expectant_parent_nominal feature is anexample of a customer life event creating a perceived need for aproduct.

Model training of different component models can identify differentfeatures as most important. For example, a model based upon a NextProduct Purchase PERM signal may identify different leading featuresthan a model based upon a Next Product Purchase TERM signal. In anembodiment, logistic regression models treated bucketed features ascategorical variables, and returned coefficients for each level (bin) ofeach bucketed feature. These coefficients provide insight into whichvariables are predictive, and that values at which they are predictive.

In an exemplary embodiment, Table 3 summarizes top predictive features,and values, for five product families. In this data, age was highlypredictive for all product families. Perm, Term, and Disability weremore marketable to young and middle-aged adults, while Fixed Annuitiesand Non-Trad Life were more likely to be purchased by older customers.Previous ownership of investment products such as Mutual Fund,Brokerage, or Third Party Money Manager were predictive of cross-salefor any product. Customers who had purchased a Term policy were morelikely to purchase additional life insurance products such as Perm orNon-Trad Life, and those with high-value policies were likely topurchase Disability. Customers who had purchased a Perm policy were morelikely to purchase Term if their Perm policy had an “Ultra” risk class,and were likely to purchase Disability if their Perm policy had a highface amount.

TABLE 3 Features and Values listed from Most to Least Predictive ProductPredictive Features Predictive Values PERM Age 20-37, 37-42, and 42-44Purchased major product type Third Party Money Manager, Mutual Fund,Brokerage, Fixed Annuity Purchased minor product type Variable Term ARTDisability premium higher Values TERM Age 26-34, 34-37, and 37-40Purchased minor product type Radius (Disability Income) Purchased majorproduct type Third Party Money Manager, Mutual Fund Perm risk UltraMedian income of zip code Above 81,100 DISABILITY Age 21-30, 30-38 and38-45 Non-Trad Life face amount Above 250,000 TERM face amount Above1,100,000 PERM face amount Above 500,000 Purchased major product typeThird Party Money Manager, Mutual Fund, Brokerage FIXED Age 63-87,58-63, and 56-58 ANNUITY Purchase major product type Perm, VariableAnnuity, Third Party Money Manager, Mutual Fund NON-TRAD Age 50-69,69-78, and 46-50 LIFE Previous minor product type Variable Term ART,Variable Term, 10 Previous major product type Mutual Fund, Brokerage,Third Party Money Manager

In the embodiment of FIG. 2, components of a predictive model pipelineinclude a predictive modeling module 200, product purchase scores module270, and reports module 280. Predictive modeling module 200 includes aplurality of component predictive models including logistic regressionmodules for each of the major product types of Table 1. These componentpredictive models includes PERM model 210, TERM model 220, DIS INC model230, FA model 240 and NTL model 250. Predictive modeling module 200 alsoincludes Gradient Boosting module 260. Product purchase scores module270 is configured to provide a PERM score, TERM score, DIS INC score, FAscore, and NTL score to reports module 280.

In the embodiment of FIG. 3, components of a predictive model pipelineinclude a predictive modeling module 300, product purchase scores module380, and reports module 390. Predictive modeling module 200 includes aplurality of component predictive models including logistic regressionmodules 310-350 corresponding to the component regression models ofpredictive modeling module 200, as well as Gradient Boosting module 360.In addition, predictive modeling module 300 includes a Lapse regressionmodel 370, which determines a lapse score for each of the customersmodeled. Product purchase scores module 380 is configured to provide aPERM score, TERM score, DIS INC score, FA score, and NTL score, as wellas LAPSE score, to reports module 390.

In the embodiment of FIG. 4, components of a predictive model pipelineinclude a predictive modeling module 400, product purchase ranks module430, feature selection module 440, motivation interpretability module450, and reports module 460. Predictive modeling module 400 includes aplurality of component regression models 410-450 corresponding to thecomponent regression models of predictive modeling module 200, as wellas decision tree model 420. Product purchase ranks module 430 isconfigured to determine a highest rank among a PERM rank, TERM rank, DISINC rank, FA rank, and NTL rank. Product purchase ranks module 430routes this highest rank as a next product purchase (NPP) rank toreports module 460, along with the associated product, and also routesthese data to feature selection module 440 for use in feature selection.

Each of the regression models of predictive machine learning modelingmodule 400 is trained to select a subset of features to use inpredictive modeling. In an embodiment, the subset of features comprisefeatures with highest predictive value, while eliminating redundantfeatures. Product-level features that contained information only after aproduct was purchased were removed to preserve predictive value. In anembodiment, Next Product Purchase predictive models were trained toinclude the following features: Customer SSN; Time period (customer yearas part of the longitudinal purchase history structure); Major producttype (e.g. Whole Life); Minor product type (e.g. Whole Life Legacy 65);Face amount; Benefit amount; Premium; Risk class; Riders. In anembodiment, certain features varied among the different predictivemodels 410-418 trained. Life policies included face amount and premiumamount, whereas disability products included benefit and premium amount.

Feature selection module 440 selects a set of highest importancefeatures from predictive features or variables processed by regressionmodels of predictive modeling module 400. In an embodiment, featureselection module 440 selects a set of features of greatest importance inpredicting the highest ranking product identified by product purchaseranks module 430. In the present disclosure, these greatest importancefeatures are also called motivations, factors, or motivating factors.

Motivation interpretability module 450 automatically generatesinterpretability data for each of the motivations selected by module440. Interpretability data helps users of the system, such as agents ofthe enterprise, to understand predictive modeling features (motivations)and to apply the motivations to pursue business objectives of the useragents. For example, this information can help an agent understand why agiven customer received a high Next Product Purchase score.

In an embodiment, motivation interpretability module 450 includes adatabase of motivation interpretability data respectively associatedwith the selected subset of features used by module 400 in predictivemodeling. In an embodiment, the database of motivation interpretabilitydata contains a lookup table with one or more motivationinterpretability data for each selected feature. The motivationinterpretability data can be created or updated during training of thecomponent regression models 410-450.

Motivation interpretability data can include, for example, explanatorytext data, such as text captions 640, 650, 660 in the likelihood tolapse dashboard 600 of FIG. 6; and graphical visualization data, such asthe sales region map 720 displayed in the likelihood to purchasedashboard 700 of FIG. 7. Motivation interpretability data can be adaptedto attributes of the features selected by module 440. Motivations caninclude general features for a priori segmentation such as cultural,demographic, geographic, and socioeconomic features. Additionally,motivations can include product-specific features.

In an embodiment, motivation interpretability module 450 canautomatically generate different motivation interpretability datadepending on context. Context can include business objective of a useragent. Examples of business objectives include closing sales tocustomers; identifying the most promising customers for cross selling(i.e., lead scoring); and training or professional development of anagent. NPP motivations and motivation interpretability data can helpinexperienced agents learn basic customer profiles best suited tocertain product families, and can offer experienced agents additionalinsights into customer behaviors to improve agents' ability to recommendproducts. Context also can distinguish motivation interpretability datagenerated for a single customer (e.g., the dashboard 600 of FIG. 6),versus data generated for a plurality of customers such as customers inthe book of business of an agent or a population of customers of theenterprise (e.g., the dashboard 700 of FIG. 7).

Motivation interpretability module 450 routes the motivating factorsidentified by module 440, with the accompanying motivationinterpretability data, to reports module 460 for display in aNext-to-Purchase report, such as a CRM dashboard.

In an embodiment, motivation data and motivation interpretability dataare included in a predictive model for lapse, such as by adding featureselection and motivation interpretability modules corresponding tomodules 440, 450 to the lapse predictive modeling pipeline of FIG. 3.

FIG. 5 shows in somewhat schematic form a dashboard 500 generated by CRMplatform 160. Dashboard 500 displays on a user interface of an agent'sdevice 570 a report of the customers in the target customer groupgenerated by the next product purchases (NPP) predictive model. Besidesinformation conventionally provided by CRM platform 160, dashboard 500provides the agent with next product purchases (NPP) data to assist theagent in cross-selling recommended products to customers with highproduct purchase scores. User interface 500 includes customer data pane450 providing information on a customer determined to have a highlikelihood of cross-sale. Customer data pane 510 may include factors ormotivations that were significant in determining a high product purchasescore. Product data pane 520 displays information on a product of theenterprise that was determined to have the highest product purchasescore among a set of product purchase scores generated for the customer.Displayed product data 520 may include factors that were significant inmaking that determination. In an embodiment, product data 520 pertainsto an NPP product recommended for cross-sale different from one or moreproducts previously purchased by the respective customer.

Window 530 of the customer service dashboard includes target customerpanes 532, 534, 536 and 538, with graphics and information on variouscustomers that were determined by the NPP predictive model to have highproduct purchase scores. In an embodiment, target customer panes 532,534, 536 and 538 have information on four customers (Customers A, B, C,D) in the agent's book of business with highest product purchase scoresdetermined by the NPP predictive model. Each pane includes a productpurchase rank 540 for the respective customer, wherein each productpurchase score represents a highest product purchase score among a setof product purchase scores generated for the customer. In an embodiment,product purchase scores 91, 83, 79, and 73 are above a threshold score70 that is a classification criterion for classifying customers in thetarget customer group. A meter indicator icon 550 has a pointer positioncorresponding to each product purchase score. A listbox 560 can displaysignificant features considered by the NPP predictive model indetermining highest product purchase ranks. In an embodiment, thesesignificant features are a set of highest importance features identifiedby the feature selection module 440 of the model pipeline of FIG. 4. Inan embodiment, the agent selects one of the target customer panes 532,534, 536 and 538 to display data for the associated customer at customerdata pane 510 and product data pane 520. Customer relationshipmanagement dashboard data on high importance features, with underlyingdetails, can provide useful insights to the agent in treatments ofcustomers in the agent's book of business.

In another embodiment, the server initiates an ongoing communicationsession with a client device of a customer in the target customer groupto display a graphical user interface (GUI) in presenting a favoredproduct and motivating factors for the customer to purchase the favoredproduct. The ongoing communication session can include email, chat,texting, over-the-top messaging, or others.

FIG. 6 shows another dashboard 600 generated by CRM platform 160.Dashboard 600 reports data on likelihood to lapse of payments for aTraditional Permanent policy owned by a given customer. Predictivemodeling of likelihood to lapse for this customer resulted in a lapserank 602 of Medium, ranking a lapse score of the customer in the Mediumtier of distribution of lapse scores for a population of customers ofthe enterprise. Dashboard 600 displays three motivation factors: FactorOne 610: Product; Factor Two 620: Billing Frequency; and Factor Three630: Includes Additional Life Insurance Rider. Dashboard 600 displaysmotivation interpretability data 640, 650, 660 for each of respectivemotivation factors 610, 620, 630. These motivation interpretability dataare generated as text captions. In addition, dashboard 600 displayscustomer financial data relevant to potential lapse, including totalRevenue to Date 604 and monthly Premium 608.

FIG. 7 shows an NPP dashboard 700 generated by CRM platform 160.Dashboard 700 reports Likelihood to Purchase: Traditional Permanent datafor a population of customers. The dashboard includes a map 720 thatdisplays customer data by region, e.g., sales territories.

In the system schematic of FIG. 8, bus 802 is in physical communicationwith I/O device 804, communication interface 806, memory 808, storagedevice 810 and central processing unit 812. Bus 802 includes a path thatpermits components within computing device 800 to communicate with eachother. Examples of I/O device 804 include peripherals and/or othermechanism that may enable a user to input information to computingdevice 800, including a keyboard, computer mice, buttons, touch screens,voice recognition, and biometric mechanisms and the like. I/O device 804also includes a mechanism that outputs information to the user ofcomputing device 800, such as a display, a light emitting diode (LED), aprinter, a speaker, and the like.

Examples of communication interface 806 include mechanisms that enablecomputing device 800 to communicate with other computing devices and/orsystems through network connections. Examples of network connectionsinclude any suitable connections between computers, such as, forexample, intranets, local area networks (LANs), virtual private networks(VPNs), wide area networks (WANs), the Internet and the like. Examplesof memory 808 include random access memory 808 (RAM), read-only memory(ROM), flash memory, and the like. Examples of storage device 810include magnetic and/or optical recording medium, ferro-electric RAM(F-RAM) hard disks, solid-state drives, floppy disks, optical discs andthe like. In one embodiment, memory 808 and storage device 810 storeinformation and instructions for execution by central processing unit812. In another embodiment, central processing unit 812 includes amicroprocessor, an application specific integrated circuit (ASIC), or afield programmable object array (FPOA) and the like. In this embodiment,central processing unit 812 interprets and executes instructionsretrieved from memory 808 and storage device 810.

The flow chart diagram of FIG. 9 shows a processor-based method 900 fortracking persons across events between the customer groups forpredicting and reporting customers with greatest likelihood of nextproduct purchase (NPP) and/or lapse behaviors. At step 902, a processorqueries an internal customer purchase history database of an enterprisecomprising purchase history information to retrieve the purchase historyinformation for each of a plurality of customers of an enterprise. Thepurchase history information comprises information on previous purchaseby each customer of one or more products from a set of products of theenterprise. In an embodiment, the purchase history information isorganized longitudinally by customer-year. In an embodiment, thepurchase history information includes information on customer's firstpurchase and date of first purchase, and information on new andcumulative purchases during each customer-year following date of firstpurchase.

At step 904, the processor queries one or more customer demographicdatabases to retrieve customer profile information corresponding to eachof the plurality of customers of the enterprise. In an embodiment, theprocessor retrieves information from an internal customer profiledatabase of the enterprise, and from an external demographic database.In an embodiment, the retrieved data is stored in a current customerprofile database. In an embodiment, the processor further queries aninternal payment database of the enterprise to retrieve information onhistory of payments by each customer for the one or products previouslypurchased by the respective customer.

At step 906, the processor executes a predictive model to determine aset of product purchase scores for each customer. The predictive modelapplies a regression model in combination with gradient boosting to thepurchase history information and the customer profile information. In anembodiment, respective product purchase scores within the set of productpurchase score are representative of likelihood that the respectivecustomer will accept a respective product from the set of products ofthe enterprise. In some embodiments of step 906, the respective productpurchase scores exclude products previously purchased by the customer.

In an embodiment of step 906, the predictive model comprises a pluralityof component predictive models, each of which is configured to targetlikelihood of purchasing one of the respective products from the set ofproducts of the enterprise. Each of the plurality of componentpredictive models determines a respective product purchase score of theset of product purchase scores.

In some embodiments of step 906, the predictive model is furtherconfigured to determine, for each of the plurality of customers, a lapsescore representative of a likelihood that the respective customer willlapse in payment for the one or more products previously purchased bythe customer.

At step 908, the processor classifies each of the plurality of customersinto one of a target customer group and a non-target customer group.This classification step applies a classification criterion to a highestproduct purchase score of the set of product purchase scores determinedfor the respective customer. In an embodiment of step 908, theclassification criterion compares size of the respective customer'shighest product purchase score to a threshold. In another embodiment ofstep 908, the classification criterion compares rank of the respectivecustomer to a given number of customers in the target customer group,wherein the rank of each customer is determined by the size of therespective customer's highest product purchase score.

In embodiments in which the predictive model additionally determines alapse score for each of the customers, step 908 further classifies eachof the customers into one of a target retention group with highestvalues of lapse scores, and a non-target retention group.

At step 910, the processor updates a graphical user interface (GUI) ofthe customers in the target customer group for display by a userinterface. In an embodiment, the GUI displays a report including thehighest product purchase scores and the products having the highestproduct purchase scores, for display by a user interface in operativecommunication with the processor.

In an embodiment of step 910, the processor is a server computer, andthe GUI is presented to an agent of the enterprise by a user interfaceof client device by displaying a customer relationship management (CRM)dashboard. In an embodiment of step 910, the processor generates areport of customers in a book of business of an agent of the enterprise,including recommended products for customers in the agent's book ofbusiness that have the highest predicted likelihood of cross-sale.

In an embodiment of step 910, the processor is a server computer, whichinitiates an ongoing communication session with a client device of acustomer in the target customer group. In an embodiment, thecommunication session is preset in advance, such as via querying a localor remote data source or a user of the client, or heuristically. Forexample, a network address, such as a phone number or screen name, canbe preset by the user of a PIM application or be availed via the server,including a chatbot application. The ongoing communication session caninclude email, chat, texting, over-the-top messaging, or others.

In an embodiment of step 910, the processor sends an email (e.g., toagents of the enterprise) with a hyperlink to a listing of the targetgroup with NPP information for a favored product. This information canbe used by agents of the enterprise in planning a marketing campaign forthe favored product. In an embodiment, the processor sends NPPinformation on a favored product to a particular agent of theenterprise, e.g., based on that agent's expertise in the favoredproduct.

In embodiments in which the predictive model additionally determines alapse score for each of the customers, step 910 further generates areport of the customers in the target retention group including thehighest lapse scores. In an embodiment, the report of the customers inthe target retention group with the highest lapse scores, furthercomprises modeled lifetime values of these customers based upon customerretention assumptions.

In an embodiment, NPP predictive models were developed over multipleiterations using training datasets and performance testing. In anexemplary embodiment, training data instances were composed of 3.1million years of purchase history data over a six-year period, for apopulation of 787,000 customers. In an embodiment, the training dataincluded cross-sales of 0.0689 cross-sales per customer. In anembodiment, during development of predictive models, performance wasevaluated by referring to the area under the curve (AUC) for allresultant Receiver Operating Curves (ROC). In various embodiments, thepredictive models are periodically retrained to deliver scores for acurrent customer base of the enterprise.

In an embodiment, a procedure for building NPP predictive modelsutilized market basket analysis machine learning to train logisticregression models (e.g., regression models 210-250, FIG. 2). Thistraining procedure performed market basket analysis mining of purchasehistory data in customer purchase history database 120 to generate aplurality of association rules. These association rules correlatedpreviously purchased products from the set of products of the enterprisewith a current purchase of an additional product of the enterprise. Eachassociation rule included an itemset of one or more previously purchasedproducts and an itemset of the current purchase of the additionalproduct of the enterprise.

Traditional machine learning via market value analysis looks at thecustomers who are most likely to purchase a given product based on theirmost current data, and analyzes what customers, overall, are the bestcandidates for a given product. The predictive machine learning modelingtechnique of the present disclosure analyzes what customers are ready tobe sold a product of the enterprise by analyzing customer purchasehistory data. The customer purchase history database tracks, for eachcustomer of the enterprise, the initial product purchased and date offirst purchase, and new and cumulative purchases of products of theenterprise during customer-years commencing from anniversaries of thecustomer's date of first purchase. In an embodiment, market basketanalysis mining of the customer purchase history database treated eachindividual customer year as a negative label within training data andtreated each sale year as a positive label within training data. Thispredictive modeling technique predicts likelihood to purchase based on acustomer's most current data, but because the predictive model istrained on customer years, the model generates a prediction whether thecustomer is likely to engage in a cross-sale in the coming customeryear.

Table 4 illustrates a simple run of association rules of an exemplarymarket basket analysis. The market value analysis extracted data fromenterprise database 110 by selecting all individual customers based onSSN, examining product data within ten major product groups. The marketvalue analysis organized data into one observation per customer, and onevariable for each product. Association rules include an antecedentitemset of one or more previously purchased products of the enterprise(lhs), and a subsequent or covariate itemset of a current purchase of anadditional product of the enterprise (rhs). The market value analysiscreated a binary indicator for each product as covariate.

TABLE 4 ASSOCIATION RULES lhs rhs support confidence lift(PERM_SEC_BRKG) => (MUTLFUND) 0.000110041 0.824104235 4.793011925 (BOE,TRAD) => (DIS_INC) 0.000170063 0.833688699 4.4818721 (BOE, NTL) =>(DIS_INC) 0.000359699 0.819623389 4.406257641 (BOE,TERM) => (DIS_INC)0.000521063 0.82111035 4.41425147 (BOE, MUTLFUND) => (DIS_INC)0.000280974 0.833548387 4.481117788 (BOE, PERM) => (DIS_INC) 0.0006406730.830326945 4.463799462 (BOE,NTLPERM) => (DIS_INC) 0.000155710.875305623 4.70560277 (BOE,MUTLFUND,TERM) => (DIS_INC) 0.0001209150.876971609 4.71455903 (BOE,PERM,TERM) => (DIS_INC) 0.0002426990.869158879 4.672558151 (BOE,MUTLFUND,PERM) => (DIS_INC) 0.0001648440.883449883 4.749385937 (GRP_NTLMUTLFUND,TRAD) => (PERM) 0.000114390.842948718 2.461696091 (GRP_NTLNTLTERM) => (PERM) 0.000120480.860248447 2.512217166 (GRP_NTLMUTLFUND,NTL) => (PERM) 0.0001448360.840909091 2.455739688 (GRP_NTL,MUTLFUND,TERM) => (PERM) 0.0002152970.874558304 2.5540068 (DIS_INC.GRP_NTLMUTLFUND,NTL) => (PERM)0.000110911 0.888501742 2.594726368 (DIS_INC.GRP_NTLMUTLFUND.TERM) =>(PERM) 0.000163104 0.892857143 2.607445615

In an embodiment, in market basket analysis data mining variousconstraints were applied to association rules to select rules ofparticular significance and interest. The market value analysis usedthreshold values of support and confidence to select association rulesof particular interest. The support of a rule is the frequency ofoccurrence of the rule in the set of all transactions. In an embodiment,calculated association rules were selected for inclusion in thepredictive model during training of the logistic regression model basedon support thresholds representing a minimum frequency within aplurality of purchase history transactions in the customer purchasehistory database.

The confidence of a rule “A=>B” is the probability that if a basketcontains A it will also contain B. In an embodiment, calculatedassociation rules were selected for inclusion in the predictive modelduring the previous training of the logistic regression model based onconfidence thresholds defining a minimum number of transactions in thecustomer purchase history database in which the antecedent itemset ofthe one or more of the previously purchased products appears.

The lift of the rule “A=>B” is a measure of the predictive power of thepremise A. Lift is a multiplier for the probability of B in the presenceof A versus the probability of B without any prior knowledge of otheritems in the market basket.

In an embodiment, NPP predictive models undergo performance testingusing various performance metrics such as the area under the ReceiverOperating Characteristic (ROC) curve, known as AUC, and lift. In anexample, predictive models were able to distinguish betweencustomer-years in which customers convert to a cross-sale and those inwhich they do not. The predictive models achieved AUC values of 0.758and higher for all product types. Logistic regression models included inan ensemble of component predictive models generated NPP predictions fordifferent major product types having a mean AUC of 0.894, indicatinghigh performing models.

FIG. 10 is an example of an ROC curve for a product family, PERM,quantifying predictive capacity of logistic regression as a tradeoffbetween true positive rate and a corresponding false positive rate. Thereceiver-operating characteristic (ROC) curve plots the true positiverate (Sensitivity) 1010 as a function of the false positive rate(100-Specificity) 1020 for different cut-off points. Each point on theROC curve 1030 represents a sensitivity/specificity pair correspondingto a particular decision threshold. An ROC curve with a higher areaunder the curve (AUC) generally indicates a higher-performing model. TheROC 1000 of FIG. 10 was obtained in testing an NPP logistic regressionmodel for the product family PERM, and has an area under the curve (AUC)1040 of 0.786, indicating a high-performing model.

FIG. 11 is an example of an ROC curve quantifying predictive capacity ofgradient boosting. The ROC of FIG. 11 was obtained in testing an NPPgradient boosting model for the product family TERM, and has an areaunder the curve (AUC) 1140 of 0.798, indicating a high-performing model.

Examples: In an exemplary embodiment, an NPP predictive model, includinga logistic regression model utilizing gradient boosting, was trainedagainst a target of TERM life insurance for a population of existingproduct owners of Massachusetts Mutual Life Insurance Company,Springfield Mass. (MassMutual). A first customer highly scored by theTERM model was a 30 year old male, who had owned MassMutual product(s)for two customer-years, and currently owned PERM (permanent lifeinsurance) and DIS INC (disability income) products. Features of thePERM product owned by the first customer were: Risk Class: PremiumUltra; Face Amount: 300K; Waiver of Premium Rider. Features of the DISINC product owned by the first customer were Job Code: 3A; Benefit: 500;Premium: $200.91.

A second customer highly scored by the TERM model was a 30 year oldmale, who had owned MassMutual product(s) for six customer-years, andcurrently owned PERM (permanent life insurance), DIS INC (disabilityincome), and Mutual Fund products. Features of the PERM product owned bythe second customer were: Product Name: Whole Life Legacy 100; RiskClass: SPNT (Super Preferred Non-Tobacco); Face Amount: 111K; Waiver ofPremium Rider. Features of the DIS INC product owned by the secondcustomer were Job Code: 2A; Benefit: 500; Premium: $206.15.

In an exemplary embodiment, an NPP predictive model, including alogistic regression model utilizing gradient boosting, was trainedagainst a target of DIS INC life insurance for a population of existingMassMutual product owners. A third customer highly scored by the DIS INCmodel was a 25 year old female, who had owned MassMutual product(s) forsix customer-years, and currently owned PERM (permanent life insurance),TERM (term life insurance), and Brokerage products. Features of the PERMproduct owned by the third customer were: Product Name: Whole LifeLegacy 20 Pay; Risk Class: SPNT (Super Preferred Non-Tobacco); FaceAmount: 555K; Waiver of Premium Rider. Features of the TERM productowned by the third customer were Product Name: Vantage Term—ART; RiskClass: UP (Ultra Premium); Face Amount: 350K; Premium: $188; Waiver ofPremium Rider.

A fourth customer highly scored by the DIS INC model was a 23 year oldmale, who had owned MassMutual product(s) for two customer-years, andcurrently owned PERM (permanent life insurance), TERM (term lifeinsurance), and Mutual Fund products. Features of the PERM product ownedby the fourth customer were: Product Name: Whole Life Legacy 20 Pay;Risk Class: SPNT (Super Preferred Non-Tobacco); Face Amount: 250K;Waiver of Premium Rider. Features of the TERM product owned by thefourth customer were Product Name: Vantage Term—20; Risk Class: UP(Ultra Premium); Face Amount: 750K; Premium: $485; Waiver of PremiumRider.

The foregoing method descriptions and the interface configuration areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the art,the steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc., are not intended to limit the orderof the steps; these words are simply used to guide the reader throughthe description of the methods. Although process flow diagrams maydescribe the operations as a sequential process, many of the operationscan be performed in parallel or concurrently. In addition, the order ofthe operations may be rearranged. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination may correspond to a return ofthe function to the calling function or the main function.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedhere may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system. Skilled artisans may implement thedescribed functionality in varying ways for each particular application,but such implementation decisions should not be interpreted as causing adeparture from the scope of the present invention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class or any combination of instructions, data structures orprogram statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters or memory contents. Information, arguments,parameters, data, etc., may be passed, forwarded or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description here.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory, computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed here may be embodied in a processor-executable software modulewhich may reside on a computer-readable or processor-readable storagemedium. A non-transitory, computer-readable or processor-readable mediaincludes both computer storage media and tangible storage media thatfacilitate transfer of a computer program from one place to another. Anon-transitory processor-readable storage media may be any availablemedia that may be accessed by a computer. By way of example, and notlimitation, such non-transitory processor-readable media may compriseRAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other tangible storagemedium that may be used to store desired program code in the form ofinstructions or data structures and that may be accessed by a computeror processor. Disk and disc, as used here, include compact disc (CD),laser disc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray disc where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable media.Additionally, the operations of a method or algorithm may reside as oneor any combination or set of codes and/or instructions on anon-transitory, processor-readable medium and/or computer-readablemedium, which may be incorporated into a computer program product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedhere may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown here but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed here.

What is claimed is:
 1. A processor-based method, comprising: executing,by a processor, a predictive machine learning model configured todetermine, for each customer record of a plurality of customer recordsincluding customer profile data stored in a customer database, a set ofproduct purchase ranks by inputting historical customer purchase dataand the customer profile data into a logistic regression model tocontinuously train the predictive machine learning model to generateassociation rules correlating previously purchased products from a setof products of an enterprise with a potential purchase, wherein thehistorical customer purchase data for each customer tracks an initialproduct purchased, date of initial product purchase, and any additionalpurchase of a product during recurring time periods following thecustomer's date of initial product purchase, wherein the continuoustraining of the logistic regression model treats each recurring timeperiod as a label within training data; inputting, by the processor intothe predictive machine learning model, a selected customer profile topredict a highest likelihood potential purchase; while the processor isassociated with an ongoing communication session with a customer device,generating, by the processor, for display by a user interface inoperative communication with the processor, content comprising anindicator for the highest likelihood potential purchase.
 2. Theprocessor-based method of claim 1, wherein each of the association rulescomprises an antecedent itemset of one or more of the previouslypurchased products from the set of products of the enterprise, and asubsequent itemset of the current purchase of the additional product ofthe enterprise.
 3. The processor-based method of claim 2, whereinparticular association rules of the generated association rules wereselected for inclusion in the predictive machine learning model duringthe continuous training of the predictive machine learning model basedon support thresholds representing a minimum frequency within aplurality of purchase history transactions in a customer purchasehistory database.
 4. The processor-based method of claim 2, whereinparticular association rules of the generated association rules areselected for inclusion in the predictive machine learning model duringthe continuous training of the predictive machine learning model basedon confidence thresholds defining a minimum number of transactions in acustomer purchase history database in which the antecedent itemset ofthe one or more of the previously purchased products appears.
 5. Theprocessor-based method of claim 1, wherein the recurring time periodsfollowing the customer's date of initial product purchase comprisecustomer-years commencing from anniversaries of the customer's date ofinitial product purchase.
 6. The processor-based method of claim 5,wherein the continuous training of the logistic regression modelgenerates the association rules by market basket analysis miningtreating each individual customer year as a negative label withintraining data and treating each customer year including a new sale as apositive label within training data.
 7. The processor-based method ofclaim 1, wherein the continuous training of the logistic regressionmodel generates the association rules by market basket analysis miningthat organized data into one observation per customer, and one variablefor each product.
 8. The processor-based method of claim 1, wherein eachof the set of product purchase ranks comprises a tier corresponding to aproduct purchase score, selected from a plurality of tiers based upon adistribution of product purchase scores for the plurality of customersof the enterprise.
 9. The processor-based method of claim 1, whereineach of the set of product purchase ranks comprises a percentileclassification of a product purchase score relative to product purchasescores for the plurality of customers of the enterprise.
 10. Theprocessor-based method of claim 1, wherein the predictive machinelearning model applies the logistic regression model using gradientboosting to the historical customer purchase data and the customerprofile data.
 11. The processor-based method of claim 1, wherein thepredictive machine learning model comprises a plurality of componentpredictive models, wherein each of the component predictive modelstargets likelihood of purchasing a respective product from a set ofproducts of the enterprise, wherein each of the plurality of componentpredictive models determines a respective one of the set of productpurchase ranks, and wherein each of the plurality of componentpredictive models is separately continuously trained to generateassociation rules correlating previously purchased products with apotential purchase.
 12. The processor-based method of claim 1, whereinthe predictive machine learning model is further configured to determinea lapse rank for each of the plurality of customer records by inputtinginformation on history of payments into the logistic regression model.13. The processor-based method of claim 12, wherein the processor isfurther configured to generate, for display by the user interface inoperative communication with the processor, content representative ofmodeled lifetime values based upon customer retention assumptions.
 14. Asystem, comprising: non-transitory machine-readable memory that storesplurality of customer records including customer profile data forcustomers of an enterprise and historical customer purchase data for theplurality of customers of the enterprise, said historical customerpurchase data comprising information on previous purchase by eachcustomer of one or more products from a set of products of theenterprise, wherein the historical customer purchase data for eachcustomer tracks an initial product purchased, date of initial productpurchase, and any additional purchase of a product during recurring timeperiods following the customer's date of initial product purchased; apredictive modeling module that stores a predictive machine learningmodel configured to determine, for each of one or more customer records,a set of product purchase ranks by applying a logistic regression modelto continuously train the predictive machine learning model to generateassociation rules correlating previously purchased products from the setof products of the enterprise with a potential purchase, wherein thecontinuous training of the logistic regression model treats eachrecurring time period as a label within training data; and a processor,configured to execute a customer targeting module, wherein the processorin communication with the non-transitory, machine-readable memory andthe predictive modeling module executes a set of instructionsinstructing the processor to: for each of the plurality of customers ofthe enterprise, determine the at least one product purchase rank byinputting the customer purchase history information and the customerprofile data into the predictive machine learning model; and while theprocessor is associated with an ongoing communication session with acustomer device, automatically generate for display by a user interfacein operative communication with the processor, content comprising anindicator for the highest likelihood potential purchase.
 15. The systemaccording to claim 14, wherein the processor is a server computer, theuser interface is included in a client device, and the contentcomprising an indicator for the highest likelihood potential purchase isdisplayed to an agent of the enterprise by the user interface of theclient device.
 16. The system according to claim 14, further comprisinga customer relationship management (CRM) platform, wherein the contentcomprising an indicator for the highest likelihood potential purchase isdisplayed to the agent of the enterprise via a dashboard of the customerrelationship management (CRM) platform.
 17. The system according toclaim 14, wherein each of the generated association rules comprises anantecedent itemset of one or more of the previously purchased productsfrom the set of products of the enterprise, and a subsequent itemset ofthe current purchase of an additional product of the enterprise.
 18. Thesystem according to claim 14, wherein particular association rules ofthe generated association rules are selected for inclusion in thepredictive machine learning model during the continuous training of thepredictive machine learning model based on confidence thresholdsdefining a minimum number of transactions in a customer purchase historydatabase in which the antecedent itemset of the one or more of thepreviously purchased products appears.
 19. The system according to claim14, wherein particular association rules of the generated associationrules are selected for inclusion in the predictive machine learningmodel during the continuous training of the predictive machine learningmodel based on confidence thresholds defining a minimum number oftransactions in a customer purchase history database in which theantecedent itemset of the one or more of the previously purchasedproducts appears.
 20. The processor-based method of claim 1, wherein thecontinuous training of the logistic regression model generates theassociation rules by market basket analysis mining treating eachrecurring time period as a negative label within training data andtreating each recurring time period including a new sale as a positivelabel within training data.